Left ventricular angiography is widely used to evaluate clinical cardiac functions such as ejection fraction, stroke volume, and wall motion abnormalities. During imaging examination, X-ray opaque contrast dye is injected into a patient left ventricle in order to visualize its variation. Medical imaging systems are typically used to capture an X-ray image sequence covering 5 to 10 cardiac cycles. In order to analyze the clinical parameters, an end-diastolic (ED) image where the left ventricle is fully filled and an end-systolic (ES) image where the ventricle is maximally contracted are retrieved from the image sequence. Image analysis such as boundary identification (contour segmentation) is performed to determine ventricular volume. Since ED and ES image retrieval is involved in analyzing cardiac functions, accurate image selection is desirable for subsequent image analysis. In known clinical work flow, ED and ES images are typically manually selected as the image frames that have the largest and smallest opacified left ventricular area, respectively. There are often hundreds of images in each left ventricular angiogram which makes manual selection of the ED and ES images burdensome, time consuming and vulnerable to human error.
Automated, intelligent and accurate ED and ES image identification and retrieval may substantially reduce user burden and increase analysis accuracy. However, automated ED and ES image identification is difficult because of low contrast and noisy background in X-ray left ventricular images and because of the wide variation and dynamic change in left ventricular shape. A system according to invention principles addresses these deficiencies and related problems.